The Bifurcation of Data Science: Making it Work

Today’s data scientists face expectations of an enormous magnitude. These professionals are singlehandedly tasked with mastering a variety of domains, from those relevant to various business units and their objectives to some of the more far-reaching principles of statistics, algorithms, and advanced analytics.

Moreover, organizations are increasingly turning to them to solve complicated business problems with an immediacy which justifies both their salaries and the scarcity with which these employees are found. When one couples these factors with the mounting pressures facing the enterprise today in the form of increasing expenditures, operational complexity, and regulatory concerns, it appears this discipline as a whole is being significantly challenged to pave the way for successfully monetizing data-driven practices.

According to Cambridge Semantics advisor Carl Reed, who spent a substantial amount of time employing data-centric solutions at both Goldman Sachs and Credit Suisse, a number of these expectations are well-intentioned, but misplaced:

“Everyone is hitting the same problem with big data, and it starts with this perception and anticipation that the concept of turning data into business intelligence that can be a differentiator, a competitive weapon, is easy to do. But the majority of the people having that conversation don’t truly understand the data dimension of the data science they’re talking about.”

Truly understanding that data dimension involves codifying it into two elements: that which pertains to science and analytics and that which pertains to the business. According to Reed, mastering these elements of data science results in a profound ability to “really understand the investment you have to make to monetize your output and, by the way, it’s the exact same investment you have to make for all these other data fronts that are pressurizing your organization. So, the smart thing to do is to make that investment once and maximize your return.”

The Problems of Data Science
The problems associated with data science are twofold, although their impacts on the enterprise are far from commensurate with each other. This bifurcation is based on the pair of roles which data scientists have to fundamentally fulfill for organizations. The first is to function as a savvy analytics expert (what Reed termed “the guy that’s got PhD level experience in the complexities of advanced machine learning, neural networks, vector machines, etc.”); the second is to do so as a business professional attuned to the needs of the multiple units who rely on both the data scientist and his or her analytics output to do their jobs better. This second dimension is pivotal and effectively represents the initial problem organizations encounter when attempting to leverage data science—translating that science into quantitative business value. “You want to couple the first role with the business so that the data scientist can apply his modeling expertise to real world business problems, leveraging the subject matter expertise of business partners so that what he comes up with can be monetized,” Reed explained. “That’s problem number one: the model can be fantastic but it has to be monetized.”

The second problem attending the work of data scientists has been well documented—a fact which has done little to alleviate it. The copious quantities of big data involved in such work require an inordinate amount of what Reed termed “data engineering”, the oftentimes manual preparation work of cleansing, disambiguating, and curating data for a suitable model prior to actually implementing data for any practical purpose. This problem is particularly poignant because, as Reed mentioned, it “isn’t pushed to the edge; it’s more pulled to the center of your universe. Every single data scientist that I’ve spoken to and certainly the ones that I have on my team consider data engineering from a data science perspective to be “data janitorial work”.

Data Governance and Data Quality Ramifications
The data engineering quandary is so disadvantageous to data science and the enterprise today for multiple reasons. Firstly, it’s work that these professionals (with their advanced degrees and analytics shrewdness) are typically over qualified for. Furthermore, the ongoing data preparation process is exorbitantly time-consuming, which keeps these professionals from spending more time working on solutions that help the business realize organizational objectives. The time consumption involved in this process becomes substantially aggravated when advanced analytics algorithms for machine learning or deep learning are involved, as Reed revealed. “I’ve had anything from young, fresh, super productive 26 year olds coming straight out of PhD programs telling me that they’re spending 80 percent of their time cleaning and connecting data so they can fit into their model. If you think about machine learning where you’ve got a massive learning set of data which is indicative of the past, especially if you’re talking about supervised [machine learning], then you need a whole bunch of connected test data to make sure your model is doing what’s expected. That’s an enormous amount of data engineering work, not modeling work.”

The necessity of data engineering is that it must take place—correctly—in order to get the desired results out of data models and any subsequent analytics or applications dependent on such data. However, it is just that necessity which frequently creates situations in which such engineering is done locally for the needs of specific data scientists using sources for specific applications, which are oftentimes not reusable or applicable for additional business units or use cases. The resulting data quality and governance complications can swiftly subtract any value attached to the use of such data.

According to Reed: “The data scientist is important, but unless you want the data scientist to be constantly distracted by cleaning data with all the negative connotations that drag with it, which is everyone’s going to use their own lingua franca, everyone’s going to use their own context, everyone’s going to clean data duplicitously, everyone’s going to clean data for themselves which, in reality, is just adding yet another entity for the enterprise to disambiguate when they look across stuff. Essentially, if you leave it to the data science they’re not turning the data into an asset. They’re turning the data into a by-product of their model.”

Recurring Centralization Wins
The solution to these data science woes (and the key to its practical implementation) is as binary as the two primary aspects of these problems are. Organizations should make the data engineering process a separate function from data science, freeing data scientists’ time to focus on the creation of data-centric answers to business problems. Additionally, they should ensure that their data engineering function is centralized to avoid any localized issues of data governance and data quality stemming from provincial data preparation efforts. There are many methods by which organizations can achieve these objectives, from deploying any variety of data preparation and self-service data preparation options, to utilizing the standards-based approach of semantic graph technologies which implement such preparation work upfront for a naturally evolving user experience. By separating the data engineering necessity as an individual responsibility in a centralized fashion, the enterprise is able to “push that responsibility hopefully towards the center of your organization where you can stitch data together, you can disambiguate it, [and] you can start treating it like an enterprise asset making it available to your data science teams which you can then associate with your businesses and pull to the edge of your organization so that they leverage clean signal many times over as consumers versus having to create clean signals parochially for themselves,” Reed remarked.

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